






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)
@dataclass
class 			lowerCAmelCase_							(					snake_case_       ):
							UpperCamelCase_		:Union[List[PIL.Image.Image], np.ndarray]
							UpperCamelCase_		:Optional[List[bool]]
							UpperCamelCase_		:Optional[List[bool]]


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipeline_if import IFPipeline
			from .pipeline_if_imgaimg import IFImgaImgPipeline
			from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
			from .pipeline_if_inpainting import IFInpaintingPipeline
			from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
			from .pipeline_if_superresolution import IFSuperResolutionPipeline
			from .safety_checker import IFSafetyChecker
			from .watermark import IFWatermarker










'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					Optional[int]       ,		A	:					int					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   str         =      TaConfig.from_json_file(A					)
							print(F"""Building PyTorch model from configuration: {config}"""					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TaForConditionalGeneration(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(A       ,		A       ,		A					)

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Union[str, Any]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Tuple             =    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




